RFID network reader scheduling optimization method based on multi-swarm particle swarm algorithm

Abstract

The invention relates to an RFID network reader scheduling optimization method based on a multi-swarm particle swarm algorithm. The method comprises the following steps: the RFID reader network is initialized; the size of a fitness value of each particle is detected; the speed of a symbiosis swarm is updated, and an individual extreme value, a swarm extreme value and a global extreme value are selected; discrete operation is carried out on the position of the particle; the maximum iteration number is set to be a termination criterion, if a reader still does not complete operation after the iteration of the time, a reader sub graph working in the time slot is deleted in an RFID interference pattern, and the step of detecting the size of the fitness value of each particle is returned; or otherwise, an RFID network reader scheduling result is outputted. Simultaneous evolution and mutual cooperation among multiple swarms can be realized, the solving efficiency is high, the operation is simple, the global search ability is strong, the convergence speed is quick, the optimization precision is high, and a new solution scheme is provided for solving the continuous optimization problem in a practical engineering application.

Description

technical field [0001] The invention relates to an RFID network reader scheduling optimization method based on multi-swarm particle swarm algorithm, which belongs to the technical field of radio frequency identification (hereinafter referred to as RFID) and also relates to the field of swarm intelligence algorithm. Background technique [0002] In large-scale RFID applications, multiple RFID readers are deployed in the work area to form a dense network of readers. Each RFID reader in the network reads and writes the electronic tags in its reading area, and sends the collected tag data to the central control system for processing. Since the dense reader network needs to cover a large number of tags in the physical environment in all directions (to prevent missed reading), it is inevitable that some readers will overlap with each other in the reading area. If the reading areas or frequency interference areas of the readers overlap with each other, conflicts between the reader...

AI Technical Summary

Problems solved by technology

However, the algorithm optimization proposed earlier has high complexity, poor robustness, and large randomness in the obtained results; although the classic particle swarm optimization algorithm has a fast optimization speed, it is easy to fall into local optimum, especially for high-dimensional optimization. The solution of the problem is not accurate enough

The above reasons lead to the fact that when the classic particle swarm optimization algorithm solves relatively complex optimization problems, its optimization performance cannot meet the satisfactory accuracy and stability requirements.

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